Enhancing Climate-Driven Urban Tree Cooling with Targeted Nonclimatic Interventions
Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We foun...
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| Published in: | Environmental science & technology Vol. 59; no. 18; p. 9082 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
13.05.2025
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| ISSN: | 1520-5851, 1520-5851 |
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| Abstract | Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We found that for every 10% increase in tree cover, surface temperatures are reduced by 0.25 °C during the day and 0.04 °C at night. Trees in humid regions exhibit the highest daytime CE, while those in arid zones demonstrate the greatest cooling effect at night. This can be explained by the difference in canopy density between the humid and arid zones. During the day, the high canopy density in the humid zone converts more solar radiation into latent heat flux. At night, the low canopy density in the arid zone intercepts less longwave radiation, which favors surface cooling. While climatic factors contribute nearly twice as much to CE as nonclimatic ones, our findings suggest that optimizing CE is possible by managing variables within specific thresholds due to their nonlinear effects. For instance, we revealed that in arid regions, an impervious surface coverage of approximately 60% is optimal, whereas in humid areas, reducing it to around 40% maximizes cooling benefits. These insights underscore the need for targeted management of nonclimatic factors to sustain tree cooling benefits and offer practical guidance for designing climate-resilient, nature-based urban strategies. |
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| AbstractList | Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We found that for every 10% increase in tree cover, surface temperatures are reduced by 0.25 °C during the day and 0.04 °C at night. Trees in humid regions exhibit the highest daytime CE, while those in arid zones demonstrate the greatest cooling effect at night. This can be explained by the difference in canopy density between the humid and arid zones. During the day, the high canopy density in the humid zone converts more solar radiation into latent heat flux. At night, the low canopy density in the arid zone intercepts less longwave radiation, which favors surface cooling. While climatic factors contribute nearly twice as much to CE as nonclimatic ones, our findings suggest that optimizing CE is possible by managing variables within specific thresholds due to their nonlinear effects. For instance, we revealed that in arid regions, an impervious surface coverage of approximately 60% is optimal, whereas in humid areas, reducing it to around 40% maximizes cooling benefits. These insights underscore the need for targeted management of nonclimatic factors to sustain tree cooling benefits and offer practical guidance for designing climate-resilient, nature-based urban strategies. Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We found that for every 10% increase in tree cover, surface temperatures are reduced by 0.25 °C during the day and 0.04 °C at night. Trees in humid regions exhibit the highest daytime CE, while those in arid zones demonstrate the greatest cooling effect at night. This can be explained by the difference in canopy density between the humid and arid zones. During the day, the high canopy density in the humid zone converts more solar radiation into latent heat flux. At night, the low canopy density in the arid zone intercepts less longwave radiation, which favors surface cooling. While climatic factors contribute nearly twice as much to CE as nonclimatic ones, our findings suggest that optimizing CE is possible by managing variables within specific thresholds due to their nonlinear effects. For instance, we revealed that in arid regions, an impervious surface coverage of approximately 60% is optimal, whereas in humid areas, reducing it to around 40% maximizes cooling benefits. These insights underscore the need for targeted management of nonclimatic factors to sustain tree cooling benefits and offer practical guidance for designing climate-resilient, nature-based urban strategies.Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We found that for every 10% increase in tree cover, surface temperatures are reduced by 0.25 °C during the day and 0.04 °C at night. Trees in humid regions exhibit the highest daytime CE, while those in arid zones demonstrate the greatest cooling effect at night. This can be explained by the difference in canopy density between the humid and arid zones. During the day, the high canopy density in the humid zone converts more solar radiation into latent heat flux. At night, the low canopy density in the arid zone intercepts less longwave radiation, which favors surface cooling. While climatic factors contribute nearly twice as much to CE as nonclimatic ones, our findings suggest that optimizing CE is possible by managing variables within specific thresholds due to their nonlinear effects. For instance, we revealed that in arid regions, an impervious surface coverage of approximately 60% is optimal, whereas in humid areas, reducing it to around 40% maximizes cooling benefits. These insights underscore the need for targeted management of nonclimatic factors to sustain tree cooling benefits and offer practical guidance for designing climate-resilient, nature-based urban strategies. |
| Author | Huang, Kangning Weng, Qihao Yao, Xihan Li, Siheng Gao, Xiaojiang Fensholt, Rasmus Yang, Wenjun Ma, Wenjuan Wang, Chenghao Yu, Zhaowu Zhou, Weiqi Chen, Jiquan Xu, Chi Rahman, Mohammad A Zhou, Yuyu Xiong, Junqi Chen, Jike |
| Author_xml | – sequence: 1 givenname: Zhaowu orcidid: 0000-0003-4576-4541 surname: Yu fullname: Yu, Zhaowu organization: IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health (WECEIPHE), Fudan University, Shanghai 200437, China – sequence: 2 givenname: Siheng surname: Li fullname: Li, Siheng organization: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China – sequence: 3 givenname: Wenjun surname: Yang fullname: Yang, Wenjun organization: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China – sequence: 4 givenname: Jiquan surname: Chen fullname: Chen, Jiquan organization: Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan 48824, United States – sequence: 5 givenname: Mohammad A orcidid: 0000-0001-9872-010X surname: Rahman fullname: Rahman, Mohammad A organization: The University of Melbourne, Burnley, Victoria 3010, Australia – sequence: 6 givenname: Chenghao surname: Wang fullname: Wang, Chenghao organization: Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma 73019, United States – sequence: 7 givenname: Wenjuan surname: Ma fullname: Ma, Wenjuan organization: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China – sequence: 8 givenname: Xihan surname: Yao fullname: Yao, Xihan organization: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China – sequence: 9 givenname: Junqi surname: Xiong fullname: Xiong, Junqi organization: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China – sequence: 10 givenname: Chi surname: Xu fullname: Xu, Chi organization: School of Life Sciences, Nanjing University, Nanjing 210023, China – sequence: 11 givenname: Yuyu surname: Zhou fullname: Zhou, Yuyu organization: Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong 999077, China – sequence: 12 givenname: Jike surname: Chen fullname: Chen, Jike organization: School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China – sequence: 13 givenname: Kangning surname: Huang fullname: Huang, Kangning organization: New York University Shanghai, Shanghai 200124, China – sequence: 14 givenname: Xiaojiang surname: Gao fullname: Gao, Xiaojiang organization: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China – sequence: 15 givenname: Rasmus surname: Fensholt fullname: Fensholt, Rasmus organization: Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark – sequence: 16 givenname: Qihao surname: Weng fullname: Weng, Qihao organization: Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China – sequence: 17 givenname: Weiqi surname: Zhou fullname: Zhou, Weiqi organization: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China |
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| Title | Enhancing Climate-Driven Urban Tree Cooling with Targeted Nonclimatic Interventions |
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